Note that binary null models return a binary matrix, even if an abundance matrix was used as input.
Arguments
- comm
Dataframe or matrix; input community data with sites (communities) as rows and species as columns. Values of each cell are the presence/absence (0 or 1) or number of individuals (abundance) of each species in each site.
- null_model
Character vector of length 1 or object of class
commsim; either the name of the model to use for generating random communities (null model), or a custom null model. For full list of available predefined null models, see the help file ofvegan::commsim(), or runvegan::make.commsim(). An object of classcommsimcan be generated withvegan::commsim()(see Examples).- n_iterations
Numeric vector of length 1; number of iterations for sequential null models. Ignored by non-sequential null models.
- thin
Numeric vector of length 1; thinning parameter used by some null models in
vegan(e.g.,quasiswap); ignored for other models.- seed
Integer vector of length 1 or NULL; random seed that will be used in a call to
set.seed()before randomizing the matrix. Default (NULL) will not change the random generator state.
Examples
set.seed(12345)
# Check list of available pre-defined null models in vegan
vegan::make.commsim()
#> [1] "r00" "c0" "r0" "r1"
#> [5] "r2" "quasiswap" "greedyqswap" "swap"
#> [9] "tswap" "curveball" "backtrack" "r2dtable"
#> [13] "swap_count" "quasiswap_count" "swsh_samp" "swsh_both"
#> [17] "swsh_samp_r" "swsh_samp_c" "swsh_both_r" "swsh_both_c"
#> [21] "abuswap_r" "abuswap_c" "r00_samp" "c0_samp"
#> [25] "r0_samp" "r00_ind" "c0_ind" "r0_ind"
#> [29] "r00_both" "c0_both" "r0_both"
# Binary null model produces binary output
data(phylocom)
cpr_rand_comm(phylocom$comm, "swap", 100)
#> sp1 sp10 sp11 sp12 sp13 sp14 sp15 sp17 sp18 sp19 sp2 sp20 sp21 sp22
#> clump1 1 0 0 0 0 0 0 1 1 0 1 0 0 0
#> clump2a 0 1 0 0 0 0 0 1 0 0 1 0 0 1
#> clump2b 1 1 0 1 0 1 0 0 0 0 1 0 0 0
#> clump4 1 0 1 0 0 0 0 1 0 1 1 0 1 0
#> even 1 0 0 0 0 0 0 1 1 0 0 1 0 0
#> random 1 0 0 1 1 0 1 0 0 0 1 0 0 0
#> sp24 sp25 sp26 sp29 sp3 sp4 sp5 sp6 sp7 sp8 sp9
#> clump1 0 0 0 1 1 0 0 1 0 0 1
#> clump2a 0 1 0 0 1 1 1 0 0 0 0
#> clump2b 0 0 0 0 1 1 0 0 0 0 1
#> clump4 0 0 0 0 0 0 1 0 0 1 0
#> even 1 0 1 0 0 0 0 0 1 0 1
#> random 0 1 0 0 0 1 1 0 0 0 0
# Quantitative null model produces quantitative output
cpr_rand_comm(phylocom$comm, "swap_count", 100)
#> sp1 sp10 sp11 sp12 sp13 sp14 sp15 sp17 sp18 sp19 sp2 sp20 sp21 sp22
#> clump1 1 0 0 2 1 0 0 0 1 0 0 0 0 0
#> clump2a 1 2 2 0 0 1 0 0 2 0 2 0 0 0
#> clump2b 1 0 0 0 0 0 1 0 0 2 2 1 0 0
#> clump4 0 0 0 0 0 0 0 5 1 0 0 0 0 0
#> even 1 0 0 1 0 0 0 0 0 0 0 1 0 1
#> random 1 1 0 0 0 3 1 3 0 0 1 0 1 0
#> sp24 sp25 sp26 sp29 sp3 sp4 sp5 sp6 sp7 sp8 sp9
#> clump1 0 0 0 0 1 0 2 0 0 0 0
#> clump2a 0 0 2 0 0 0 0 0 0 0 0
#> clump2b 0 2 0 0 0 1 1 0 0 0 1
#> clump4 1 0 0 0 1 2 0 0 0 1 1
#> even 0 0 0 0 0 0 0 1 1 0 2
#> random 1 1 0 1 1 0 1 0 0 0 0
# How to use a custom null model
# 1. Define a randomizing function, e.g. re-sample the matrix while
# preserving total number of presences (same as the "r00" model)
randomizer <- function(x, n, ...) {
array(replicate(n, sample(x)), c(dim(x), n))
}
# 2. Generate a commsim object
cs_object <- vegan::commsim(
"r00_model",
fun = randomizer, binary = TRUE,
isSeq = FALSE, mode = "integer"
)
# 3. Generate the null community
cpr_rand_comm(phylocom$comm, cs_object, 100)
#> sp1 sp10 sp11 sp12 sp13 sp14 sp15 sp17 sp18 sp19 sp2 sp20 sp21 sp22
#> clump1 0 1 0 0 1 0 1 0 0 0 0 1 0 1
#> clump2a 0 0 0 0 1 0 0 1 1 0 0 0 0 1
#> clump2b 0 0 0 0 0 0 0 0 0 0 1 0 0 0
#> clump4 0 1 1 0 1 1 0 0 0 1 1 1 0 1
#> even 0 0 1 0 1 0 0 1 1 1 1 0 0 1
#> random 1 1 0 0 1 0 0 0 0 0 0 0 0 0
#> sp24 sp25 sp26 sp29 sp3 sp4 sp5 sp6 sp7 sp8 sp9
#> clump1 1 0 0 1 0 0 0 0 0 0 0
#> clump2a 1 0 0 0 0 0 1 0 1 0 0
#> clump2b 0 0 1 0 1 0 1 1 0 0 0
#> clump4 1 0 0 0 1 0 0 0 0 1 1
#> even 0 0 0 1 0 0 0 0 0 0 1
#> random 0 0 1 1 1 1 0 1 0 0 0